Intelligent gallery management for biometrics

ABSTRACT

A system provides intelligent gallery management for biometrics. A first gallery is obtained that includes biometric and/or other information on a population of people. An application is identified. A subset of the population of people is identified based on the application. A second gallery is derived from the first gallery by pulling the information for the subset of the population of people without pulling the information for the population of people not in the subset. Biometric identification (such as facial recognition) for the application may then be performed using the second gallery rather than the first gallery. In this way, the system is improved as less time is required for biometric identification, fewer device resources are used, and so on.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is a divisional patent application of U.S. patentapplication Ser. No 16/731,154, filed Dec. 31, 2019 and titled“Intelligent Gallery Management for Biometrics,” which is a continuationpatent application of U.S. patent application Ser. No. 16/048,094, filedJul. 27, 2018 and titled “Intelligent Gallery Management forBiometrics,” which is a nonprovisional patent application of and claimsthe benefit of U.S. Provisional Patent Application No. 62/538,348, filedJul. 28, 2017 and titled “Intelligent Gallery Management forBiometrics,” the disclosures of which are hereby incorporated herein byreference in their entireties.

FIELD

The described embodiments relate generally to biometrics. Moreparticularly, the present embodiments relate to intelligent gallerymanagement for biometrics.

BACKGROUND

Biometric identification systems may identify people using biometrics.Biometrics may include fingerprints, irises, eyes, faces, voices, gaits,pictures, or other identifying characteristics about a person. Abiometric identification system may capture a biometric using abiometric reader and identify a person by comparing the capturedinformation against stored information. For example, a camera maycapture an image of a fingerprint and compare the image of thefingerprint against stored fingerprint images.

SUMMARY

The present disclosure relates to techniques for intelligent gallerymanagement for biometrics. A first gallery may be obtained that includesbiometric and/or other information on a population of people. Anapplication may be identified. A subset of the population of people maybe identified based on the application. A second gallery may be derivedfrom the first gallery by pulling the information for the subset of thepopulation of people without pulling the information for the populationof people not in the subset. Biometric identification (such as facialrecognition) for the application may then be performed using the secondgallery rather than the first gallery. In this way, a device or systemthat performs such a method may be improved as less time may be requiredfor biometric identification, fewer device resources may be used, and soon.

In various implementations, an intelligent biometric gallery managementsystem includes at least one non-transitory storage medium that storesinstructions and at least one processor. The at least one processorexecutes the instructions to obtain a biometric gallery that includesbiometric information for a population; ascertain an application forwhich to use the biometric gallery to identify people; identify a subsetof the population based on the application; derive, from the biometricgallery, an application specific biometric gallery for the applicationthat includes the biometric information for the subset of thepopulation; and use the application specific biometric gallery toidentify the people for the application.

In some examples, the at least one processor determines a person was notidentified using the application specific biometric gallery andcommunicates with the biometric gallery to identify the person. Invarious such examples, the application specific biometric gallery isstored locally and the biometric gallery is stored remotely. In othersuch examples, the at least one processor adds the biometric informationfor the person to the application specific biometric gallery.

In various examples, identification using the application specificbiometric gallery is more accurate than identification using thebiometric gallery. In some examples, the application is a firstapplication; the application specific biometric gallery is a firstapplication specific biometric gallery; and the at least one processorderives, from the biometric gallery, a second application specificbiometric gallery for a second application. In numerous examples, thebiometric gallery is at least ten times larger than the applicationspecific biometric gallery.

In some implementations, an intelligent biometric gallery managementsystem includes at least one non-transitory storage medium that storesinstructions and at least one processor. The at least one processorexecutes the instructions to obtain a biometric gallery that includesbiometric information for a population; ascertain an application forwhich to use the biometric gallery to identify people; determine datathat is common to a subset of the population to which the application isapplicable and is not common to a remainder of the population; derive,from the biometric gallery, an application specific biometric galleryfor the application that includes the biometric information that isassociated with the data; and provide access to the application specificbiometric gallery for biometric identification.

In various examples, the at least one processor updates the applicationspecific biometric gallery. In numerous examples, the at least oneprocessor adds a portion of the biometric information from the biometricgallery to the application specific biometric gallery. In some suchexamples, the at least one processor adds the portion of the biometricinformation from the biometric gallery to the application specificbiometric gallery upon occurrence of a change to the application, achange to the biometric gallery, or elapse of a time period.

In numerous examples, the at least one processor removes a portion ofthe application specific biometric gallery. In some such examples, theat least one processor removes the portion of the application specificbiometric gallery after adding to the application specific biometricgallery. In various such examples, the at least one processor removesthe portion of the application specific biometric gallery to maintain aminimum gallery size.

In numerous implementations, an intelligent biometric gallery managementsystem includes at least one non-transitory storage medium that storesinstructions and at least one processor. The at least one processorexecutes the instructions to obtain a biometric gallery that includesbiometric information for a population; create an application specificbiometric gallery by pulling the biometric information for a subset ofthe population from the biometric gallery, the subset of the populationassociated with an application for which the biometric gallery can beused to identify people; and provide access to the application specificbiometric gallery for biometric identification.

In some examples, the application specific biometric gallery is a facialrecognition biometric gallery and the at least one processor creates afingerprint recognition gallery from the biometric gallery. In variousexamples, the application is identifying ticketed people and the subsetof the population is the ticketed people.

In numerous examples, the subset of the population is previouslyidentified people. In some such examples, the at least one processorremoves the biometric information for a previously identified personupon elapse of a time period without subsequent identification.

In various examples, the application specific biometric gallery is afirst application specific biometric gallery, the at least one processorcreates a second application specific biometric gallery from thebiometric gallery that is larger than the first application specificbiometric gallery, and the second application specific biometric galleryis used for identification upon failure to identify using the firstapplication specific biometric gallery.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure will be readily understood by the following detaileddescription in conjunction with the accompanying drawings, wherein likereference numerals designate like structural elements.

FIG. 1 depicts an Intelligent Gallery Management (IGM) system.

FIG. 2 depicts an example of the high level of accuracy that is achievedthrough “high-touch” biometric matching.

FIG. 3 depicts a process whereby IGM logic may be applied to createapplication specific galleries that may be used by biometric matchingservices.

FIG. 4 depicts the IGM in more depth.

FIG. 5 depicts a flow chart illustrating a first example method ofintelligent gallery management for biometrics.

FIG. 6 depicts a flow chart illustrating a second example method ofintelligent gallery management for biometrics.

FIG. 7 depicts a flow chart illustrating a third example method ofintelligent gallery management for biometrics.

DETAILED DESCRIPTION

Reference will now be made in detail to representative embodimentsillustrated in the accompanying drawings. It should be understood thatthe following descriptions are not intended to limit the embodiments toone preferred embodiment. To the contrary, it is intended to coveralternatives, modifications, and equivalents as can be included withinthe spirit and scope of the described embodiments as defined by theappended claims.

The description that follows includes sample systems, apparatuses,methods, and computer program products that embody various elements ofthe present disclosure. However, it should be understood that thedescribed disclosure may be practiced in a variety of forms in additionto those described herein.

The following disclosure relates to techniques for intelligent gallerymanagement for biometrics. A first gallery may be obtained that includesbiometric and/or other information on a population of people. Anapplication may be identified. A subset of the population of people maybe identified based on the application. A second gallery may be derivedfrom the first gallery by pulling the information for the subset of thepopulation of people without pulling the information for the populationof people not in the subset. Biometric identification (such as facialrecognition) for the application may then be performed using the secondgallery rather than the first gallery. In this way, a device or systemthat performs such a method may be improved as less time may be requiredfor biometric identification, fewer device resources may be used, and soon.

These and other embodiments are discussed below with reference to FIGS.1-7. However, those skilled in the art will readily appreciate that thedetailed description given herein with respect to these Figures is forexplanatory purposes only and should not be construed as limiting.

FIG. 1 depicts an Intelligent Gallery Management (IGM) system 100. Thesystem 100 includes an intelligent gallery management device 101. Insome implementations, the system 100 may also include an applicationdevice 109 with which the intelligent gallery management device 101 isoperable to communicate via one or more communication networks 110.

The intelligent gallery management device 101 may create or otherwiseobtain a master enrollment gallery 106 that includes biometric and/orother information on a population of people. The intelligent gallerymanagement device 101 may identify one or more applications and identifyone or more subsets of the population of people based on theapplication. The intelligent gallery management device 101 may deriveone or more application specific galleries 107 from the masterenrollment gallery 106 by pulling the information for the subset of thepopulation of people without pulling the information for the populationof people not in the subset. Biometric identification (such as facialrecognition) for the application may then be performed (such as by theintelligent gallery management device 101, the application device 109,and so on) using the application specific gallery 107 rather than themaster enrollment gallery 106. In this way, a device or system thatutilizes these techniques may be improved as less time may be requiredfor biometric identification, fewer device resources may be used, and soon.

The intelligent gallery management device 101 may include one or moreprocessing units 104 or other processors or controllers, non-transitorystorage media 105, communication components 108, and/or othercomponents. The non-transitory storage media 105 may store the masterenrollment gallery 106 and/or the application specific gallery 107. Theprocessing unit 104 may execute instructions stored in thenon-transitory storage media 105 to perform various functions. Suchfunctions may include, but are not limited to, obtaining or creating themaster enrollment gallery 106, identifying applications or populationsubsets, deriving or generating application specific galleries 107,performing biometric identification, communicating with the applicationdevice 109 via the communication component 108, and so on.

Similarly, the application device 109 may include one or more processingunits 111 or other processors or controllers, non-transitory storagemedia 113, communication components 112, and/or other components. Thenon-transitory storage media 113 may store the application specificgallery 107 received from the intelligent gallery management device 101.The processing unit 111 may execute instructions stored in thenon-transitory storage media 113 to perform various functions. Suchfunctions may include, but are not limited to, receiving the applicationspecific gallery 107, performing biometric identification, communicatingwith the intelligent gallery management device 101 via the communicationcomponent 112, and so on.

Although the system 100 is illustrated and described as includingparticular components that perform particular functions, it isunderstood that this is an example. Various configurations are possibleand contemplated without departing from the scope of the presentdisclosure. These and other features are described in more detail below.

Types of Biometric Matching

Embodiments discussed herein may generally apply to two types ofbiometric matching, namely “verification” matching and “identification”matching. Verification matches are made to determine a person's identityfrom among a group of known people culled from a larger pool, whileidentification matches are made to determine an individual's identityfrom the larger pool itself (e.g., rather than against a subset of thepool, as in verification).

Verification may assume knowledge of the person presenting themselves,and may verify his or her identity using biometric matching. Thisprocess may use a small number of potential matches, as the verificationprocess itself may deeply narrow down the potential matching candidatesin advance. This is referred to as 1-to-1 or 1-to-few matching, where“few” may mean less than 20 potential candidates to match against.Verification may be a useful biometric matching solution when there issome knowledge introduced at the exact time of the biometric match, likean identification card with a name or other personal identifier.

In these cases, a workflow may include a) presenting an identificationtoken with a personal identifier on it, b) reading the personalidentifier and querying a biometric repository to identify potentialmatches, c) doing a biometric match against the returned potentialmatches, and d) responding with a match result. One example of this typeof use may be when a person puts their ATM card in a bank machine, andthe bank ATM then takes a photo of their face and compares it to theregistered face associated with the accounts associated with thepresented ATM card. In that case, the facial recognition matching isonly comparing the photo to the faces associated with that account, notall faces for all accounts registered at that bank.

Identification may assume no advanced knowledge of who is presentingthemselves to be identified. This process may be referred to as1-to-many or 1-to-n. Identification operations may be much more complexthan Verification operations, as they may rely on using the biometricsthemselves, and they may search against the entire gallery of enrolledindividuals, which may measure into the hundreds of millions ofidentities.

The process of doing 1-to-many identifications against a group ofbiometrically enrolled individuals may consist of the following steps:

1. Build a “Gallery” of enrollment templates: a. A Gallery may be a setof biometric templates for enrolled individuals. Each template may beassociated with an enrolled identity. b. A Template may be a binarystring that is produced by running an algorithm against a biometricimage (e.g., fingerprint, iris, face).

2. Place the Gallery in an infrastructure that has a Matching Algorithm:If the gallery size is large, this infrastructure may be very large, asmeasured by the number of servers or core processes that may run inparallel to handle large gallery size or concurrent identificationrequests.

3. As Probe images are sent to the matching infrastructure, the matchingalgorithm may attempt to find an identity within some acceptablematching threshold. A Probe image may be an image taken at the time anindividual is to be biometrically identified. It may be turned into aProbe template using the same or similar logic to create gallerytemplates, and that probe template may be introduced to the matchingalgorithm and may produce match results.

Challenges to overcome with biometric matching solutions may include 1)Accuracy, 2) Latency, and 3) Throughput.

Accuracy may be determined by a measure of False Positive Rate (FPR) andFalse Negative Rate (FNR). A given matching algorithm may have definedrates of these measures, and they may be variable based on the number oftemplates in the enrollment gallery. As the gallery grows, it may bechallenging to keep the accuracy stable, perhaps even high.

Latency may mean the time it takes for a single identificationoperation, and Throughput may mean how many identifications can be donewithin a given period of time. A given matching algorithm may beoptimized for (or may generally address) one or both of these measures.

Type of Biometrics

Some biometrics, by their very nature, may be faster or more accuratethan others. Likewise, some biometrics may be slower or less accuratethan others. Organizations that choose to implement biometricidentification may take many factors into account as to which biometricsthey want to consider.

Some major factors to consider may include: Importance of accurateidentification, user experience, timeliness of response, and cost ofinfrastructure.

More mature and “high-touch” biometric matching solutions likefingerprints and irises, may deliver high accuracy and speed withsmaller cost to infrastructure, but may deliver such results at theexpense of user experience. The biometrics may operate within awell-defined set of quality and acceptance criteria, and the biometriccapture devices may be specialized to capture only good images underideal sets of conditions.

An example of the high level of accuracy that is achieved through“high-touch” biometric matching is shown in FIG. 2. This is taken fromFIG. 15 of NISTIR 8034(http://nvlpubs.nist.gov/nistpubs/ir/2014/NIST.IR.8034.pdf) 2014Fingerprint Vendor Technology Evaluation, which is hereby incorporatedby reference in its entirety.

This data shows that while pegging the False Positive IdentificationRate (FPIR) to 0.001%, the best performing vendor was able to achieve aFalse Negative Identification Rate (FNIR) of 0.27% searched against anenrollment set of 1.6 million subjects. This is a very high level ofaccuracy.

Newer, and more desirable “low-touch” biometric matching solutions, likefacial recognition, may deliver a very desirable user experience, butmay deliver such at the cost of accuracy, speed, and cost toinfrastructure. The biggest challenge with some of the “low-touch”biometric matching solutions may be that they are impacted by many moreexternal factors that may impact results, lighting for facialrecognition, for example. The combination of both less mature matchingalgorithms as well as the high level of deviation of biometric imagesfor the same identity because of external conditions may lead to asignificant impact in both accuracy and speed for these “low-touch”biometrics. This accuracy drop-off may become very relevant as the sizeof the gallery grows. In some facial recognition matching algorithms, agallery size of 50,000 may be where accuracy begins to degradedramatically to the point of becoming useless. This may be extremelylimiting when a desired gallery size of 100 million is desired for anIdentification operation.

The following defines systems, methods, apparatuses, and computerprogram products that may perform processes to take galleries ofextremely large size, such as in excess of 100 million, and work with“low-touch” biometrics, in a way that may achieve the accuracy, latency,and throughput results of mature “high-touch” biometrics, withoutsacrificing on user experience or high cost of infrastructure.

Although the above describes using the techniques herein to achieveaccuracy, latency, and throughput results of mature “high-touch”biometrics using “low-touch” biometrics, it is understood that this isan example. In various implementations, techniques discussed herein maybe used in the context of any kind of biometrics and/or combination ofbiometrics.

FIG. 3 depicts a high-level process 300 whereby IGM 322 logic may beapplied (such as on a continual basis) to create application specificgalleries 307 that may be used by biometric matching services 321. TheIGM 322 logic may understand the maximum size of the applicationspecific galleries to achieve the required accuracy and latency results.The IGM 322 logic, using data that may be available from one or multipleexternal resources, may continually update the application specificgalleries 307 to ensure that those potential identities from the masterenrollment gallery 306 or “master gallery” that could possibly beidentified at that point in time are available without includingidentities that could not possibly be identified at that point in time.By applying this constant logic, and managing the size of theapplication specific gallery 307, the associated biometric matchingalgorithm may match against a gallery that may be within the sizeconstraints to achieve the accuracy and latency results that aredesired.

Example:

Given a master enrollment gallery 306 of 10 million identities where thedesired biometric for identification is face. Utilizing a best-in-classfacial recognition solution (which may be one that is commerciallyavailable), the maximum gallery size to achieve the same accuracy as onewould get using an average commercially available fingerprint solution,may be 20,000. In this example, the application may be to use facialrecognition to identify airline passengers as they approach a securitycheckpoint. The IGM, in this case, may be set to manage an applicationspecific gallery of size no bigger than 20,000. The IGM 322 may beintegrated with the airline's reservation systems, and may be restrictedto identities in the application specific gallery that are associatedwith an airline reservation for that time window, and that airport. Thetime window may be variable and systematically modified to achieve theexample maximum gallery size of 20,000.

FIG. 4 depicts the IGM 322 in more depth 400. The IGM 322 may beconfigured, for each application specific gallery 307 that it serves, amaximum gallery size, and the criteria for an identity to be included inthat gallery at a particular point in time. The IGM 322 may be connecteddirectly to the master enrollment gallery 306 as well as one or moreexternal systems or data sources. The job of the IGM 322 may be tocontinually keep all application specific galleries 306 up to date basedon the data received from the external sources.

Example:

An IGM 322 may be set up to serve 3 major league ballparks. Each maywish to use facial recognition for both security screening and ticketentry purposes. As a fan approaches the entry checkpoint, their photomay be taken and an identification operation may be performed against anapplication specific gallery 307 for that team and that game. So, theremay be one application specific gallery 307 for baseball team A, one forbaseball team B, and one for baseball team C. The IGM 322 may beconfigured so that all 3 application specific galleries may be no largerthan 20,000. The IGM 322 may be connected to a common Master EnrollmentGallery 306. For baseball team A, the IGM 322 may also be connected tothe baseball team A ticketing system via API (application programminginterface), to the baseball team A Customer Relationship Management(CRM) system database, and to a purpose-built file reading utility thatcontains a VIP list. The baseball team A logic may be to restrictidentities put in the application specific gallery to those that are a)in the VIP list, b) have been to a baseball team A game in the last 30days, or c) are associated with tickets purchased for this particulargame. Since tickets may be purchased at any time up to and including thestart of the game, c) may be a real-time feed that continually orotherwise updates the baseball team A application specific gallery 307.

Example:

An IGM 322 may be configured to identify members of a private bar. Sinceusers under twenty-one years of age would not be legally permitted toenter the private bar, an application specific gallery 307 may bederived by pulling only information for people who are at leasttwenty-one years of age from a master enrollment gallery 306. As peopleunder twenty-one years of age would not be permitted to be members,there would be no need to include their information in the applicationspecific gallery 307 that is used to identify members and, thus, therewould be no need to compare a biometric for a person to be identifiedagainst their information.

Further, the IGM 322 may have access to information regarding thecurrent city in which people in the master enrollment gallery 306 arelocated. This information may be derived from check ins on social media,cell phone location services, transportation manifests, and so on. Aspeople who are not located within a certain distance (such as a hundredmiles) of the private bar could not be attempting to gain entry, theapplication specific gallery 307 may be derived by pulling onlyinformation for people who are at least twenty-one years of age and whoare located within the certain distance of the private bar from themaster enrollment gallery 306. In this way, the size of the applicationspecific gallery 307 could be further reduced in order to improveidentification time and accuracy as well as reduce storage space for theapplication specific gallery 307.

In some examples of such implementations, the application specificgallery 307 may be generated on a repeating basis, such as once per day.This may allow the application specific gallery 307 to be limited topeople located within the certain distance at the same time that itallows for updating when people relocate.

In various examples of such implementations, the master enrollmentgallery 306 and/or a larger application specific gallery 307 (which maybe stored locally or remote) may be used as a backup to biometricidentification using the application specific gallery 307. A person notincluded in the application specific gallery 307 may still be includedin the master enrollment gallery 306 and/or the larger applicationspecific gallery 307. If the person is not able to be identified usingthe application specific gallery 307, the master enrollment gallery 306and/or the larger application specific gallery 307 may then bereferenced. This may allow use of the smaller and faster applicationspecific gallery 307 for the majority of identifications, resorting tothe master enrollment gallery 306 and/or the larger application specificgallery 307 in the few cases when identification using the applicationspecific gallery 306 is not possible. This may result in fasteridentification times than use of the master enrollment gallery 306and/or the larger application specific gallery 307 all the time, thoughnot as fast as if the application specific gallery were only used, whilebalancing the ability to identify more people.

Example:

An IGM 322 may be configured to identify people registered with agrocery chain savings program. An application specific gallery 307 foreach grocery store location may be derived by pulling people identifiedfrom stored information about the savings program as frequenting thatgrocery store location from a master enrollment gallery 306 includingall registered members. People may then be identified by comparing abiometric obtained from them at checkout to the application specificgallery 307. If they are not included in the application specificgallery, the master enrollment gallery 306 may be referenced to identifythem. That person may then be added to the application specific gallery307 since evidence has been obtained that they have used that grocerystore location. If a certain amount of time has elapsed since peoplehave visited the grocery store location, they may be removed from theapplication specific gallery 307 for that grocery store location. Inthis way, the application specific gallery may be updated with ageinformation in order to manage application specific gallery size andusefulness.

Example:

An IGM 322 may be configured to first attempt to identify people using afirst type of biometric, such as facial recognition, and then supplementthis process by attempting to identify people using a second type ofbiometric, such as fingerprint, if the people cannot be identified bythe first method. This may be used in a facility where identificationmay be performed upon entry and then subsequently for other uses, suchas at an airport where people may first be identified at a securityscreening and then at businesses or gates within the airport once theyhave been screened. The two attempts may use different galleries. Uponfirst identification, the person may not be included in the gallery forthe first type of biometric and may not be identified by comparing thefirst type of biometric obtained from them with the gallery for thefirst type of biometric. The second type of biometric may then beobtained and, if the person is identified by comparing the second typeof biometric to the gallery for the second type of biometric, the firsttype of biometric obtained from the person may then be added to thegallery for the first type of biometric. When subsequent attempts aremade to identify the person using the first type of biometric, they maybe identified using the first type of biometric and the second type ofbiometric may not then be obtained.

Example:

An IGM 322 may be configured to provide different tiers of service todifferent customers. For example, a venue may provide first, second, andthird class entry for descending prices. A master enrollment gallery 306may include all known people. A first application specific gallery 307may be derived from the master enrollment gallery 306 by pullinginformation for people who have paid for first class entry. A secondapplication specific gallery 307 may be derived from the masterenrollment gallery 306 by pulling information for people who have paidfor first or second class entry. A third application specific gallery307 may be derived from the master enrollment gallery 306 by pullinginformation for people who have paid for first, second, or third classentry. Though people who have paid for first class entry could beidentified using any of the three application specific galleries 306 andpeople who have paid for second class entry could be identified usingeither of the second or third application specific galleries 306,attempts may be made to identify a person using the first, second, andthird application specific galleries 306 in sequence.

As the second is larger than the first and the third is larger than thesecond, more time may be used to compare against the second than thefirst and the third than the second. As such, quicker identification maybe provided to people who have paid for first class entry than second,and similarly quicker identification may be provided to people who havepaid for second class entry than third. This faster identification maybe a perk that is provided to incentivize people to pay more to obtainhigher classes of entry.

A method 500 of intelligent gallery management for biometrics mayinclude one or more of the following operations (such as is shown inoperations 510-550 of the flow chart depicted in FIG. 5).Characteristics applicable to a subset of a master gallery that arerelevant to an identification situation (such as security screening fora particular airport on a particular day, ticket validation for aparticular ticketed event at a particular venue, and so on) may beidentified. An application specific gallery may be created by obtainingthe subset of the master gallery using the identified characteristics.Biometric identification (such as facial recognition) for theidentification situation may then be performed using the applicationspecific gallery rather than the master gallery. In this way, a deviceor system that performs such a method 500 may be improved as less timemay be required for biometric identification, fewer device resources maybe used, and so on.

For example, characteristics may be identified at 510, an applicationspecific gallery may be created at 520, and a determination may be madeat 530 whether or not to identify a biometric. If so, biometricidentification may be performed at 550 using the application specificgallery. Otherwise, the flow may wait at 540 before again determining at530 whether or not to identify a biometric.

In various examples, this example method 500 may be implemented as agroup of interrelated software modules or components that performvarious functions discussed herein. These software modules or componentsmay be executed by one or more computing devices. For example, thesesoftware modules or components may be executed by the intelligentgallery management device 101 or the application device 109 of FIG. 1.

Although the example method 500 is illustrated and described asincluding particular operations performed in a particular order, it isunderstood that this is an example. In various implementations, variousorders of the same, similar, and/or different operations may beperformed without departing from the scope of the present disclosure.

For example, 550 is illustrated and described as using the applicationspecific gallery for biometric identification. However, in somesituations, biometric information for a person may not be stored in theapplication specific gallery. In such a situation, if biometricidentification using the application specific gallery fails, anothergallery may be used for biometric identification. Various configurationsare possible and contemplated without departing from the scope of thepresent disclosure.

In numerous embodiments, an intelligent biometric gallery managementsystem may include at least one non-transitory storage medium thatstores instructions and at least one processor. The at least oneprocessor may execute the instructions to obtain a biometric gallerythat includes biometric information for a population; create anapplication specific biometric gallery by pulling the biometricinformation for a subset of the population from the biometric gallery,the subset of the population associated with an application for whichthe biometric gallery can be used to identify people; and provide accessto the application specific biometric gallery for biometricidentification.

In some examples, the application specific biometric gallery may be afacial recognition biometric gallery and the at least one processor maycreate a fingerprint recognition gallery from the biometric gallery. Invarious examples, the application may be identifying ticketed people andthe subset of the population may be the ticketed people.

In numerous examples, the subset of the population may be previouslyidentified people. In some such examples, the at least one processor mayremove the biometric information for a previously identified person uponelapse of a time period without subsequent identification.

In various examples, the application specific biometric gallery may be afirst application specific biometric gallery, the at least one processormay create a second application specific biometric gallery from thebiometric gallery that is larger than the first application specificbiometric gallery, and the second application specific biometric gallerymay be used for identification upon failure to identify using the firstapplication specific biometric gallery.

Another method 600 of intelligent gallery management for biometrics mayinclude one or more of the following operations (such as is shown in theoperations 610-650 of the flow chart depicted in FIG. 6). A firstgallery may be obtained that includes biometric and/or other informationon a population of people. An application may be identified. A subset ofthe population of people may be identified based on the application. Asecond gallery may be derived from the first gallery by pulling theinformation for the subset of the population of people without pullingthe information for the population of people not in the subset.Biometric identification for the application may then be performed usingthe second gallery rather than the first gallery. In this way, a deviceor system that performs such a method 600 may be improved as less timemay be required for biometric identification, fewer device resources maybe used, and so on.

For example, characteristics applicable to a subset of a master gallery(i.e., a first gallery) relevant to an identification situation may beidentified at 610, an application specific gallery (i.e., a secondgallery) may be created by obtaining the subset using the identifiedcharacteristics at 620, and a determination may be made at 630 whetheror not to identify a biometric. If so, biometric identification may beperformed at 650 using the application specific gallery. Otherwise, theflow may wait at 640 before again determining at 630 whether or not toidentify a biometric.

In various examples, this example method 600 may be implemented as agroup of interrelated software modules or components that performvarious functions discussed herein. These software modules or componentsmay be executed by one or more computing devices. For example, thesesoftware modules or components may be executed by the intelligentgallery management device 101 or the application device 109 of FIG. 1.

Although the example method 600 is illustrated and described asincluding particular operations performed in a particular order, it isunderstood that this is an example. In various implementations, variousorders of the same, similar, and/or different operations may beperformed without departing from the scope of the present disclosure.

For example, 620 is illustrated and described as creating oneapplication specific gallery. However, in various implementations, anumber of different application specific galleries may be created. Insome implementations, different application specific galleries may becreated for different purposes. Various configurations are possible andcontemplated without departing from the scope of the present disclosure.

In various embodiments, an intelligent biometric gallery managementsystem may include at least one non-transitory storage medium thatstores instructions and at least one processor. The at least oneprocessor may execute the instructions to obtain a biometric gallerythat includes biometric information for a population; ascertain anapplication for which to use the biometric gallery to identify people;identify a subset of the population based on the application; derive,from the biometric gallery, an application specific biometric galleryfor the application that includes the biometric information for thesubset of the population; and use the application specific biometricgallery to identify the people for the application.

In some examples, the at least one processor may determine a person wasnot identified using the application specific biometric gallery andcommunicate with the biometric gallery to identify the person. Invarious such examples, the application specific biometric gallery may bestored locally and the biometric gallery may be stored remotely. Inother such examples, the at least one processor may add the biometricinformation for the person to the application specific biometricgallery.

In various examples, identification using the application specificbiometric gallery may be more accurate than identification using thebiometric gallery. In some examples, the application may be a firstapplication; the application specific biometric gallery may be a firstapplication specific biometric gallery; and the at least one processormay derive, from the biometric gallery, a second application specificbiometric gallery for a second application. In numerous examples, thebiometric gallery may be at least ten times larger than the applicationspecific biometric gallery.

Still another method 700 of intelligent gallery management forbiometrics may include one or more of the following operations (such asis shown in operations 710-780 of the flow chart depicted in FIG. 7). Amaster gallery may be obtained. The master gallery may includeinformation on a population of people. An application for biometricidentification may be identified. A subset of the population of peoplethat the application is applicable to may be determined. Informationcommon to the subset but not the rest of the population of people may bedetermined. An application specific gallery may be generated byincluding people from the master gallery who have the determinedinformation but not including people from the master gallery who do nothave the determined information. Biometric identification for theidentification situation may then be performed using the applicationspecific gallery rather than the master gallery. In this way, a deviceor system that performs such a method 700 may be improved as less timemay be required for biometric identification, fewer device resources maybe used, and so on.

For example, a master gallery may be obtained at 710. An application forbiometric identification may be identified at 720. A subset of a mastergallery the application is applicable to may be determined at 730.Information common to the subset may be determined at 740. A galleryincluding people from the master gallery who have the determinedinformation may be generated at 750. Then, a determination may be madeat 760 whether or not to identify a biometric. If so, biometricidentification may be performed at 780 using the gallery. Otherwise, theflow may wait at 770 before again determining at 760 whether or not toidentify a biometric.

In various examples, this example method 700 may be implemented as agroup of interrelated software modules or components that performvarious functions discussed herein. These software modules or componentsmay be executed by one or more computing devices. For example, thesesoftware modules or components may be executed by the intelligentgallery management device 101 or the application device 109 of FIG. 1.

Although the example method 700 is illustrated and described asincluding particular operations performed in a particular order, it isunderstood that this is an example. In various implementations, variousorders of the same, similar, and/or different operations may beperformed without departing from the scope of the present disclosure.

For example, 720 is illustrated and described as obtaining a mastergallery. However, in some implementations, the master gallery may begenerated instead of being obtained. Various configurations are possibleand contemplated without departing from the scope of the presentdisclosure.

In some embodiments, an intelligent biometric gallery management systemmay include at least one non-transitory storage medium that storesinstructions and at least one processor. The at least one processor mayexecute the instructions to obtain a biometric gallery that includesbiometric information for a population; ascertain an application forwhich to use the biometric gallery to identify people; determine datathat is common to a subset of the population to which the application isapplicable and is not common to a remainder of the population; derive,from the biometric gallery, an application specific biometric galleryfor the application that includes the biometric information that isassociated with the data; and provide access to the application specificbiometric gallery for biometric identification.

In various examples, the at least one processor may update theapplication specific biometric gallery. In numerous examples, the atleast one processor may add a portion of the biometric information fromthe biometric gallery to the application specific biometric gallery. Insome such examples, the at least one processor may add the portion ofthe biometric information from the biometric gallery to the applicationspecific biometric gallery upon occurrence of a change to theapplication, a change to the biometric gallery, or elapse of a timeperiod.

In numerous examples, the at least one processor may remove a portion ofthe application specific biometric gallery. In some such examples, theat least one processor may remove the portion of the applicationspecific biometric gallery after adding to the application specificbiometric gallery. In various such examples, the at least one processormay remove the portion of the application specific biometric gallery tomaintain a minimum gallery size.

The techniques discussed herein regarding reduced gallery size bygenerating an application specific biometric gallery from a mastergallery may be more applicable to biometric identification and may notprovide as much improvement for biometric verification. This may be dueto the nature of biometric verification involving matching againstinformation for just one person to verify that person is who theypurport to be. Contrasted with biometric identification where theidentity of the person is unknown until identification is performed, andinvolving matching against information of potentially a large number ofpeople, biometric verification may not be improved by reducinginformation that biometrics or various digital representations thereofare matched against.

In the context of this disclosure, terms such as “biometricinformation,” “biometric data,” “information about biometrics,” “dataregarding biometrics,” and/or similar terms may refer to any kind ofinformation related to biometrics. This may include, but is not limitedto, full and/or partial images of biometrics, digital representations ofbiometrics, hashes, encodings of biometrics, and/or any other digital orother data structure that may indicate and/or store informationregarding one or more biometrics.

In some implementations, the method may further include updating theapplication specific biometric gallery based on the detection of one ormore conditions. Such conditions may include, but are not limited to,failed biometric identification attempts, changes in circumstances thatwere used to determine the subset, elapse of a time period, changes tothe master gallery, changes to the application, and so on. Variousarrangements are possible and contemplated without departing from thescope of the present disclosure.

Aspects of the present disclosure may be performed by one or moredevices, such as one or more computing devices, that may be configuredas part of a system. For example, one or more computing devices thatperform one or more aspects of this disclosure may be part of a cloudcomputing system, cooperative computing arrangement, and so on. Suchdevices may include one or more processing units, one or morenon-transitory storage media (which may take the form of, but is notlimited to, a magnetic storage medium; optical storage medium;magneto-optical storage medium; read only memory; random access memory;erasable programmable memory; flash memory; and so on), and/or othercomponents. The processing unit may execute one or more instructionsstored in the non-transitory storage medium to perform one or moreprocesses that utilize one or more of the techniques disclosed hereinfor intelligent gallery management for biometrics.

The present disclosure recognizes that biometric and/or other personaldata is owned by the person from whom such biometric and/or otherpersonal data is derived. This data can be used to the benefit of thosepeople. For example, biometric data may be used to conveniently andreliably identify and/or authenticate the identity of people, accesssecurely stored financial and/or other information associated with thebiometric data, and so on. This may allow people to avoid repeatedlyproviding physical identification and/or other information.

The present disclosure further recognizes that the entities who collect,analyze, store, and/or otherwise use such biometric and and/or otherpersonal data should comply with well-established privacy policiesand/or privacy practices. Particularly, such entities should implementand consistently use privacy policies and practices that are generallyrecognized as meeting or exceeding industry or governmental requirementsfor maintaining securely and privately maintaining biometric and/orother personal data, including the use of encryption and securitymethods that meets or exceeds industry or government standards. Forexample, biometric and/or other personal data should be collected forlegitimate and reasonable uses and not shared or sold outside of thoselegitimate uses. Further, such collection should occur only afterreceiving the informed consent. Additionally, such entities should takeany needed steps for safeguarding and securing access to such biometricand/or other personal data and ensuring that others with access to thebiometric and/or other personal data adhere to the same privacy policiesand practices. Further, such entities should certify their adherence towidely accepted privacy policies and practices by subjecting themselvesto appropriate third party evaluation.

Additionally, the present disclosure recognizes that people may blockthe use of, storage of, and/or access to biometric and/or other personaldata. Entities who typically collect, analyze, store, and/or otherwiseuse such biometric and and/or other personal data should implement andconsistently prevent any collection, analysis, storage, and/or other useof any biometric and/or other personal data blocked by the person fromwhom such biometric and/or other personal data is derived.

As described above and illustrated in the accompanying figures, thepresent disclosure relates to techniques for intelligent gallerymanagement for biometrics. A first gallery may be obtained that includesbiometric and/or other information on a population of people. Anapplication may be identified. A subset of the population of people maybe identified based on the application. A second gallery may be derivedfrom the first gallery by pulling the information for the subset of thepopulation of people without pulling the information for the populationof people not in the subset. Biometric identification (such as facialrecognition) for the application may then be performed using the secondgallery rather than the first gallery. In this way, a device or systemthat performs such a method may be improved as less time may be requiredfor biometric identification, fewer device resources may be used, and soon.

In the present disclosure, the methods disclosed may be implemented assets of instructions or software readable by a device. Further, it isunderstood that the specific order or hierarchy of steps in the methodsdisclosed are examples of sample approaches. In other embodiments, thespecific order or hierarchy of steps in the method can be rearrangedwhile remaining within the disclosed subject matter. The accompanyingmethod claims present elements of the various steps in a sample order,and are not necessarily meant to be limited to the specific order orhierarchy presented.

The described disclosure may be provided as a computer program product,or software, that may include a non-transitory machine-readable mediumhaving stored thereon instructions, which may be used to program acomputer system (or other electronic devices) to perform a processaccording to the present disclosure. A non-transitory machine-readablemedium includes any mechanism for storing information in a form (e.g.,software, processing application) readable by a machine (e.g., acomputer). The non-transitory machine-readable medium may take the formof, but is not limited to, a magnetic storage medium (e.g., floppydiskette, video cassette, and so on); optical storage medium (e.g.,CD-ROM); magneto-optical storage medium; read only memory (ROM); randomaccess memory (RAM); erasable programmable memory (e.g., EPROM andEEPROM); flash memory; and so on.

The foregoing description, for purposes of explanation, used specificnomenclature to provide a thorough understanding of the describedembodiments. However, it will be apparent to one skilled in the art thatthe specific details are not required in order to practice the describedembodiments. Thus, the foregoing descriptions of the specificembodiments described herein are presented for purposes of illustrationand description. They are not targeted to be exhaustive or to limit theembodiments to the precise forms disclosed. It will be apparent to oneof ordinary skill in the art that many modifications and variations arepossible in view of the above teachings.

1-7. (canceled)
 8. An intelligent biometric gallery management system,comprising: at least one non-transitory storage medium that storesinstructions; and at least one processor that executes the instructionsto: maintain a biometric gallery that includes biometric information fora population; generate an application specific biometric gallery bypulling a portion of the biometric information corresponding to a subsetof the population from the biometric gallery, the subset of thepopulation associated with an application for which the biometricgallery can be used to identify people; use the application specificbiometric gallery for biometric identification; and maintain a maximumsize of the application specific biometric gallery by removing firstdata from the application specific biometric gallery upon adding seconddata to the application specific biometric gallery.
 9. The system ofclaim 8, wherein the maximum size is dependent upon a biometric type forwhich the application specific biometric gallery is configured.
 10. Thesystem of claim 9, wherein the maximum size is further dependent upon abiometric matcher used with the application specific biometric galleryfor the biometric identification.
 11. The system of claim 8, whereinmaintaining the maximum size enables a false negative identificationrate to be kept below a threshold number.
 12. The system of claim 8,wherein the maximum size is dependent upon a latency threshold.
 13. Thesystem of claim 8, wherein the maximum size is dependent upon anaccuracy threshold.
 14. The system of claim 8, wherein the at least oneprocessor allows addition to the application specific biometric gallerywithout removal when the application specific biometric gallery is belowthe maximum size. 15-20. (canceled)
 21. The system of claim 8, whereinthe at least one processor is operable to update the applicationspecific biometric gallery.
 22. The system of claim 21, wherein the atleast one processor updates the application specific biometric galleryby adding the second data.
 23. The system of claim 21, wherein the atleast one processor updates the application specific biometric galleryby removing the first data.
 24. An intelligent biometric gallerymanagement system, comprising: at least one non-transitory storagemedium that stores instructions; and at least one processor thatexecutes the instructions to: use an application specific biometricgallery for biometric identification, the application specific biometricgallery generated from a biometric gallery that includes biometricinformation for a population by pulling a portion of the biometricinformation corresponding to a subset of the population from thebiometric gallery, the subset of the population associated with anapplication for which the biometric gallery can be used to identifypeople; updating the application specific biometric gallery in: a firstmanner when the application specific biometric gallery is below amaximum size; and a second manner when the application specificbiometric gallery is below a maximum size.
 25. The system of claim 24,wherein: the first manner comprises adding data without reducing a sizeof the application specific biometric gallery; and the second mannercomprises adding the data after reducing the size of the applicationspecific biometric gallery.
 26. The system of claim 24, wherein thesubset of the population has airline reservations within a time window.27. The system of claim 26, wherein the at least one processor adjuststhe time window to maintain the maximum size of the application specificbiometric gallery.
 28. The system of claim 24, wherein the maximum sizeof the application specific biometric gallery comprises data for twentythousand individuals.
 29. The system of claim 24, wherein theapplication comprises airport security screening.
 30. An intelligentbiometric gallery management system, comprising: at least onenon-transitory storage medium that stores instructions; and at least oneprocessor that executes the instructions to: generate an applicationspecific biometric gallery by pulling a portion of biometric informationcorresponding to a subset of a population from a biometric gallery, thesubset of the population associated with an application for which thebiometric gallery can be used to identify people; use the applicationspecific biometric gallery for biometric identification; add first datato the application specific biometric gallery; and remove second datafrom the application specific biometric gallery upon determining thatadding the second data to the application specific biometric gallerycauses the application specific biometric gallery to exceed a maximumsize.
 31. The system of claim 30, further comprising adding third datato the application specific biometric gallery upon determining that theapplication specific biometric gallery has fallen below the maximumsize.
 32. The system of claim 30, wherein the first data comprises aperson's biometric information.
 33. The system of claim 30, wherein thesecond data comprises a person's biometric information.